Comparative Linear Classification Splicing

The conventional Fisher linear classification analysis has been investigated by numerous researchers and this has led to different modification or splicing due to non- robustness when the assumptions are violated and also when the data set contains influential observations.  This paper adduced a winsorized procedure to robustify the probability base classification approach.  The comparative classification performance of the Fisher linear classification analysis and its spliced versions when the data set are contaminated are investigated. The simulation results revealed that the robust Fisher's approach based on the minimum covariance determinant estimates outperformed the other procedures; a good competitor to this technique is the winsorized probability base classification technique. Though, the robust Fisher's technique using the minimum covariance determinant estimates breakdown for mixture contamination. On a general note, the conventional Fisher's approach and the probability base technique performed comparable.